1. Field of the Invention
The present invention relates to manufacturing order scheduling and more particularly to synchronizing production and routing of orders.
2. Description of the Related Art
Scheduling work in a manufacturing environment is a complex process. Most factories use an automated planning and scheduling system to ensure that customer demand is satisfied in a timely manner with minimum inventory. To achieve this goal, such planning requires that work for each manufacturing line is efficiently scheduled, that the appropriate materials needed to complete each task performed are available when needed on the manufacturing line, and that products are manufactured in the order that the products are needed. To produce a manufacturing schedule, customer orders should be received and analyzed, priorities should be assigned to items to be manufactured, manufacturing resources should be allocated, work should be scheduled, raw materials and/or parts should be obtained and delivered to the manufacturing line, work in progress should be tracked, and variability in availability of raw materials and/or parts must be handled. Many manufacturing facilities plan and manage these many tasks by combining multiple computerized planning and scheduling systems with paper-based management systems.
An example of a widely-used commercially available automated planning and scheduling system is 32 Technologies, Inc.'s Factory Planner and Demand Fulfillment and Supply Chain Planner. The 32 Factory Planner generates work schedules and material requirements schedules using customer-provided inputs of demand and inventory. The 32 Demand Fulfillment application helps organizations to quote and promise order delivery to customers in real-time while obeying customer constraints on lot sizes, number of shipments, and time between shipments. The 32 Supply Chain Planner helps provide a global view of the entire supply chain from sourcing to delivery. These products handle the complicated scheduling for large, distributed, complex manufacturing environments. However, any automated planning and scheduling system can only produce accurate results if inputs to the system are accurate.
Most businesses schedule manufacturing activities based upon forecasts of demand for products. Work is typically scheduled on a daily or weekly basis to meet demand predicted based on past sales. Inputs to the automated planning and scheduling system are demand forecasts.
To ensure that demand is satisfied, most factories maintain inventories of both parts and/or raw materials. Each type of inventory typically includes stock to accommodate the average usage rate and stock to meet variations in demand. However, maintaining high inventory levels does not necessarily guarantee that the right inventory is available when and where it is needed. A material delivery schedule is needed that delivers material to the manufacturing line prior to the time the material is needed during manufacturing.
Furthermore, due to limited space in most factories and the expense of maintaining warehouses of inventory, it is desirable to maintain only the minimum inventory necessary to meet demand. Some factories operate on a build-to-customer-order model where no product is manufactured unless it has been ordered by a customer. This model enables the factory to operate with minimal inventory of finished products, but does not address the inventories of materials.
In addition to minimizing material inventory, it is also desirable to minimize material handling to ensure that materials are delivered to the right location at the right time.
Problems with scheduling manufacturing activities are exacerbated in a mass production manufacturing environment for commodities that are built to customer orders. The term commodity describes a mass-produced unspecialized product. In such an environment, the timeframes for manufacturing and delivery activities may be sub-hourly. Demand forecasts do not reliably predict material needs at this level, and schedules based upon demand forecasts become less and less accurate as time elapses between the time the work is scheduled and the time the work is initiated on the manufacturing line. Nor do demand forecasts respond to variations in material needs resulting from atypical customer orders. Scheduling based upon demand forecasts does not provide the responsiveness to changes in inventory and work schedules needed to ensure that materials are delivered to the right place at the right time.
It is known for a new plan generated from demand and supply data to consider previously requested materials (e.g., Purchase Orders or similarly generated requests) and consider previously unrequested availability to be available at fixed lead times (e.g., at X business days or Y hours in the future). A current planned request typically relied on an assumption of the static nature of all previous demand and supply inputs that were provided to the current plan generation.
More specifically, referring to FIG. 1, labeled prior art, when executing a planning cycle x−1, a planned request from a supplier does not take into account actual deliveries made by the supplier during the planning cycle (e.g., a supplier may only deliver material in pallets of 100 despite having a supply request of 90). Accordingly, at the end of the planning cycle x, the manufacturer may actually be in receipt of 10 more items than actually needed. This excess is acknowledged in planning cycle x+1 and actually taken into account when the x+1 plan is executed.
It is known for transportation management software vendors to implement business rules logic to make logistics routing decisions. These systems often focus on batch processing. Known systems optimize logistics through a process of analyzing a batch of pending shipments and identifying opportunities to consolidate shipments and to make parcels into less than truckload (LTL) shipments, LTL shipments into truckload shipments, etc. These processes use batches of orders to optimize the shipping logistics.